Detection and surgical treatment of the early stages of tissue malignancy are usually curative. In contrast, diagnosis and treatment in late stages often have deadly results. Likewise, proper classification of the various stages of cancer progression is imperative for efficient and effective treatment.
The aim of embedding algorithms is to construct low-dimensional feature-space embeddings of high-dimensional data sets. The low-dimensional representation is easier to visualize and helps provide easily interpretable representations of intra-class relationships, so that objects, which are closer to one another in the high dimensional ambient space, are mapped to nearby points in the embedded output.
In view of the above, there is a need for a sytematic medical decision support system capable of finding new classes within datasets which do not necessarily fit within conventional datasets.